{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Matplotlib是一个数学绘图库\n",
    "后续将使用Plotly包，生成的图表适合在数字设备上显示\n",
    "这份makedown就介绍如何绘制简单的折线图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 安装Matplotlib\n",
    "pip安装"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "$ python3 -m pip install --usere matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 绘制简单的折线图\n",
    "使用平方数1，4，6，16，25来绘制这个图表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import paddle\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "\r\n",
    "squares = [1,4,9,16,25]\r\n",
    "fig, ax = plt.subplots()\r\n",
    "ax.plot(squares)\r\n",
    "\r\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "首先导入paddle，然后导入模块pyplot，并指定别名plt，以免反复输入pyplot，模块pyplot包含很多用于生成图表的函数\n",
    "\n",
    "创建一个名为squares的列表，在其中存储用来做图表的数据，调用函数subplots()\n",
    "\n",
    "变量fig表示整张图片，变量ax表示图片中的各个图表\n",
    "\n",
    "接下来调用方法plot()，绘制图表\n",
    "\n",
    "函数plt.show()显示图表"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 修改标签文字和线条粗细\n",
    "通过一些定制来改善这个图表的可读性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import paddle\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "\r\n",
    "squares = [1,4,9,16,25]\r\n",
    "fig, ax = plt.subplots()\r\n",
    "ax.plot(squares, linewidth=3)\r\n",
    "\r\n",
    "# 设置图表标题并给坐标轴加上标签\r\n",
    "ax.set_title(\"Square number\",fontsize=24)\r\n",
    "ax.set_xlabel(\"Value\",fontsize=14)\r\n",
    "plt.ylabel(\"Square of value\",fontsize=14)\r\n",
    "\r\n",
    "# 设置刻度标记的大小\r\n",
    "ax.tick_params(axis='both',labelsize=14)\r\n",
    "\r\n",
    "plt.show()\r\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "linewidth决定了plot()绘制的线条粗细\n",
    "\n",
    "fontsize指定图表中各种文字的大小\n",
    "\n",
    "ax.set_xlabel为x轴设置标题，方法tick_params()设置刻度的样式，实参影响x轴与y轴上的刻度（axis='both'），字号设置为14\n",
    "\n",
    "标签最好用英文，不然会不显示，要么使用plt.xlabel()方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 校正图形\n",
    "折线图终点指向4.0的平方为25，下面来修复这个问题\n",
    "\n",
    "向plot()提供一系列数时，改变默认第一个数据点对应x坐标值为0的行为，向plot（）同时提供输入值和输出值\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2366: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  return list(data) if isinstance(data, collections.MappingView) else data\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd8a1170a90>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import paddle\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "\r\n",
    "input_values = [1,2,3,4,5]\r\n",
    "squares = [1,4,9,16,25]\r\n",
    "\r\n",
    "fig,ax = plt.subplots()\r\n",
    "ax.plot(input_values,squares,linewidth = 3)\r\n",
    "\r\n",
    "# 设置图表标题并给坐标轴加上标签"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 使用内联样式\n",
    "Matplotlib提供了很多已经定义好的样式，获知系统中可使用哪些样式，可在终端会话中执行如下命令"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    ">>>import paddle\r\n",
    ">>>import matplotlib.plt.pyplot as plt\r\n",
    ">>>plt.style.available"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 使用scatter()绘制散点图并设置样式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import paddle\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "\r\n",
    "plt.style.use('seaborn')\r\n",
    "fig,ax = plt.subplots()\r\n",
    "ax.scatter(2,4)\r\n",
    "\r\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "下面来设置图表的样式，添加标签加标题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AIiIikWIIICIiEimGACIiIpFiCCAiIhIphgAiIiKRYgggIiISKYYAIiIikWIIICIiEimGACIiIpFiCCAiIhIphgAiIiKRYgggIiISKYYAIiIikWIIICIiEimGACIiIpFiCCAiIhIphgAiIiKR0lkIWLBgAezt7WscT0hIQP/+/eHq6op+/frh0KFDijG5XI41a9agR48ecHV1xYABA3D8+HFtlE1ERPTa0EkIyMjIQFxcXI3jmZmZmDJlCsLDw3H69GlMmDABkydPxqVLlwAA33//PbZt24avvvoKycnJ+OCDDxAeHo579+5pqwUiIqJXntZDgFwux6xZszBixIga19mxYwc6d+6MHj16wMjICN27d0fHjh2xc+dOAEC9evUwdepUtGnTBgYGBhgyZAgA4Pz581rpgYiI6HWg9RCwbds2GBsbIyAgoMZ1Lly4AEdHR6VlDg4OSEtLAwAMHz4cffr0UYzl5uairKwMNjY2mimaiIjoNaSvzZ3l5uZi9erViImJqXW9vLw8mJmZKS0zNzfHo0ePqqxbUVGBGTNmoFOnTnB2dq71eS0sTKCvr1f3wtXI2tpUp/vXNfbP/sWM/bP/l41WQ8DChQsRHByMFi1aICsrq9Z1BUF45vM9efIEERERyM3NxcaNG5+5/qNHxSrXqgnW1qbIySnUaQ26xP7ZP/tn/2Kly/5rCx9a+zjg1KlTSEtLQ1hY2DPXtbCwQF5entKyvLw8WFlZKR7n5+djyJAhKCsrw6ZNm2Bubq72momIiF5nWjsTEB8fj+zsbHh5eQH4v3f6np6e+Pzzz5U+43dyckJ6errS9mlpaYrT/TKZDKNHj4atrS0WLVoEfX2tntAgIiJ6LWjtTEBUVBQSExMRFxeHuLg4rFu3DgAQFxcHX19f+Pn5ISkpCQAQGhqKpKQkHD58GDKZDAcPHsSZM2cQGhoKANi4cSPKy8sZAIiIiF6A1v6CmpubK52yLy8vBwA0btwYAHD9+nUUFz/9zL5Vq1ZYvnw5li1bhoiICNjZ2SE6Ohq2trYAgJ07d+Lu3btwdXVV2kdYWBjGjh2rjXaIiIheeRJBlRl4rwldT0rhxBj2z/7Zv1ixf5FPDCQiIqKXC0MAERGRSDEEEBERiRRDABERkUgxBBAREYkUQwAREZFIMQQQERGJFEMAERGRSDEEEBERiRRDABERkUgxBBAREYkUQwAREZFIMQQQERGJFEMAERGRSDEEEBERiRRDABERkUgxBBAREYkUQwAREZFIMQQQERGJFEMAERGRSDEEEBERiRRDABERkUgxBBAREYkUQwAREZFIMQQQERGJFEMAERGRSDEEEBERiRRDABERkUgxBBAREYkUQwAREZFIMQQQERGJFEMAERGRSDEEEBERiRRDABERkUgxBBAREYkUQwAREZFIMQQQERGJFEMAERGRSDEEEBERiRRDABERkUgxBBAREYkUQwAREZFIMQQQERGJFEMAERGRSDEEEBERiRRDABERkUgxBBAREYmUzkLAggULYG9vX+N4QkIC+vfvD1dXV/Tr1w+HDh1SGi8pKcHnn38Oe3t7JCUlabpcIiKi145OQkBGRgbi4uJqHM/MzMSUKVMQHh6O06dPY8KECZg8eTIuXboEAMjJyUFQUBAqKiq0VTIREdFrR+shQC6XY9asWRgxYkSN6+zYsQOdO3dGjx49YGRkhO7du6Njx47YuXMnAODRo0f47LPPMH36dG2VTURE9NrRegjYtm0bjI2NERAQUOM6Fy5cgKOjo9IyBwcHpKWlAQCkUil69+6t0TqJiIhed/ra3Flubi5Wr16NmJiYWtfLy8uDmZmZ0jJzc3M8evTohfZvYWECfX29F3qOF2VtbarT/esa+2f/Ysb+2f/LRqshYOHChQgODkaLFi2QlZVV67qCIKh9/48eFav9OevC2toUOTmFOq1Bl9g/+2f/7F+sdNl/beFDax8HnDp1CmlpaQgLC3vmuhYWFsjLy1NalpeXBysrK02VR0REJDpaOxMQHx+P7OxseHl5Afi/d/qenp74/PPP0adPH8W6Tk5OSE9PV9o+LS0Nzs7O2iqXiIjotae1MwFRUVFITExEXFwc4uLisG7dOgBAXFwcfH194efnp7jePzQ0FElJSTh8+DBkMhkOHjyIM2fOIDQ0VFvlEhERvfa0dibA3Nwc5ubmisfl5eUAgMaNGwMArl+/juLip5/Zt2rVCsuXL8eyZcsQEREBOzs7REdHw9bWFgCwZs0arF27VvFcH3/8MSQSCcLCwjB27FhttURERPRKkwiamIH3ktL1pBROjGH/7J/9ixX7F/nEQCIiInq5MAQQERGJFEMAERGRSKkcAnbs2IHQ0FD4+voCAEpLSxEdHc0v8SEiInpFqRQCVqxYgW+++QZ+fn7Izc0FADx+/Bi//PILli5dqtECiYiISDNUCgG7du3CunXrMHz4cEgkEgCApaUloqOjceDAAY0WSERERJqhUggoLi5GixYtqiy3tLREfn6+2osiIiIizVMpBLRu3RqxsbFVln/77bdo1aqV2osiIiIizVPpjoGRkZEYM2YMNm/ejLKyMowaNQqXL19GUVGR0p37iIiI6NWhUgjw8PDAgQMHsG/fPjg6OsLY2Bje3t7o27cvzMzMNF0jERERaYDK3x3QuHFjfPLJJ5qshYiIiLRIpRAQFBSkuCqgOj/99JPaCiIiIiLtUCkE+Pj4KD2uqKjArVu3kJqaimHDhmmkMCIiItIslULAZ599Vu3y06dPY+fOnWotiIiIiLTjhb47wMPDA8eOHVNXLURERKRFKp0JuHLlSpVlT548waFDh3h1ABER0StKpRAQEBAAiUQCQRCUlpuammL27NmaqIuIiIg0TKUQ8PPPP1dZZmRkBEtLS9Srx28jJiIiehXVGAKePHmi+NnS0rLadUpLSwEA9evXV3NZREREpGk1hgBXV9da7w0AAIIgQCKRICMjQ+2FERERkWbVGAI2bdqkzTqIiIhIy2oMAR4eHio9wX//+1+V1yUiIqKXh0oTAwVBwE8//YT09HTIZDLF8vv37yMtLU1jxREREZHmqDS1f8GCBfjyyy9x//59xMfHo7CwECkpKXj06BFWrlyp6RqJiIhIA1Q6E5CQkIAdO3agefPmaNu2Lb766itUVFRg3rx5uHfvnqZrJCIiIg1Q6UxAcXExmjdvDgDQ09NDeXk59PT0MH78eERHR2u0QCIiItIMlUJAixYtsG3bNsjlcjRr1gyHDh0C8PReAnl5eRotkIiIiDRDpRAQERGBxYsXo7i4GMOGDcOUKVPQu3dv9O/fH927d9d0jURERKQBKs0J6NSpE06dOgUjIyMEBwfjrbfeQlpaGt566y306tVL0zUSERGRBqgUAiZNmoSAgAB07doV+vr66NixIzp27Kjp2oiIiEiDVAoBADBlyhTo6emhR48eCAgIQIcOHZ55W2EiIiJ6eakUApYtW4aysjL8/vvvOHz4MCZNmoR69erBz88P/v7+cHNz03SdREREpGYqnwkwMDCAt7c3vL29IZfLkZSUhNWrV2Pz5s38AiEiIqJXkMohAABKSkpw4sQJHDlyBMePH4exsTGGDh2qqdqIiIhIg1QKAbt27cKRI0dw6tQpmJmZoVevXlizZg0/BiAiInqFqRQCVqxYgV69emH9+vVo166dpmsiIiIiLVApBPz666+8EoCIiOg1o9IdAxkAiIiIXj8qhQAiIiJ6/dQYAh4/fqz4uaioSCvFEBERkfbUGAK6dOkCuVwOAOjcubPWCiIiIiLtqHFioI2NjeLLgsrKyjBhwoQan2TlypUaKY6IiIg0p8YQsHr1auzYsQMFBQUAABMTE60VRURERJpXYwho2bIlpk2bBgCQy+VYuHCh1ooiotdDqawCP6fexq3sIkCvHlAhx9s2puju/haMDPV0XR6R6Kl0n4BFixahsLAQJ06cQFZWFiQSCezs7NClSxfUr19f0zUS0StGLhew89gVpF7KQW5+idJYcuZ9/HL2Dtyl1gj2aYV69XgJMpGuqBQCzpw5g7CwMMjlcjRr1gwAcOfOHdSvXx8//vgj7OzsNFkjEb1C5HIBa+PSkXoxp8Z1cvNLkJhyGw8KSjCmvxODAJGOqHSfgCVLlmDo0KFITk5GfHw84uPjcerUKfTv3x/z589/rh0vWLAA9vb2NY4nJCSgf//+cHV1Rb9+/XDo0CHFmCAIWLVqFXr06IF27dph6NChuHz58nPVQUTqtfPYlVoDwD+duZiDnceuaLgiIqqJSiHg4sWLGDNmDPT0/u8zPENDQ4SHhyMtLa3OO83IyEBcXFyN45mZmZgyZQrCw8Nx+vRpTJgwAZMnT8alS5cAAFu2bMHu3buxevVq/Prrr3Bzc8Po0aNRWlpa51qISH1KZRVIvaRaAKiUeikHpbIKDVVERLVRKQRYWloiOzu7yvKHDx/C2Ni4TjuUy+WYNWsWRowYUeM6O3bsQOfOndGjRw8YGRmhe/fu6NixI3bu3AkA2Lp1K4YNGwZ7e3uYmJhg3LhxijkLRKQ7P6ferjIH4Fly80tw9I8sDVVERLVRKQS8//77CAsLw759+5CRkYGMjAzs27cPY8aMgbe3d512uG3bNhgbGyMgIKDGdS5cuABHR0elZQ4ODkhLS0NJSQmuXLkCBwcHxZiBgQGkUulznZUgIvW5lf18dxe9ca9QzZUQkSpUmhgYGRkJiUSCuXPnKu4b0KBBA/Tv3x///e9/Vd5Zbm4uVq9ejZiYmFrXy8vLg5mZmdIyc3NzPHr0CPn5+RAEAebm5tWO18bCwgT6+rq9LMna2lSn+9c19v+a96/3fF9HUk+v3uv/2kAEx/8Z2P/L179KIcDQ0BBTp07F1KlTUVBQAJlMBisrqzp/u+DChQsRHByMFi1aICur9tN/giC80Hh1Hj0qrvM26mRtbYqcHPG+42H/Iui/Qv5cm8kr5K/9ayOK418L9q+7/msLH3WO7WZmZmjUqFGdA8CpU6eQlpaGsLCwZ65rYWGBvLw8pWV5eXmwsrLCG2+8gXr16lU7bmlpWaeaiEi93rZp+Fzb2dq8fO+QiMRAa18lHB8fj+zsbHh5ecHT0xMDBgwAAHh6emL//v1K6zo5OSE9PV1pWVpaGpydnWFkZITWrVsrff4vk8mQmZkJFxcXzTdCRDXq7t4cjczrNlnY2twY3du9paGKiKg2WgsBUVFRSExMRFxcHOLi4rBu3ToAQFxcHHx9feHn54ekpCQAQGhoKJKSknD48GHIZDIcPHgQZ86cQWhoKABg0KBBiImJwaVLl1BcXIzly5fjzTff5LcdEumYkaEe3KXWddrGTWoNIwPeQphIF1SaE6AO5ubmSpP5ysvLAQCNGzcGAFy/fh3FxU8/s2/VqhWWL1+OZcuWISIiAnZ2doiOjoatrS0A4MMPP0Rubi5GjhyJgoICuLm54ZtvvoGBgYG22iGiGgT7tEJuQYlKNwxqZ//01sFEpBsSQcUZdidPnsTu3btx//59xMTEoLy8HPHx8YrT+q8CXU9K4cQY9i+W/mv77gDg6UcAbiL77gAxHf/qsP+Xc2KgSmcCYmJiEB0djX79+uHw4cMAgAcPHmD16tXIzc3FqFGj1FMpEb0W6tWT4MPurfFB1xY4+kcWbtwrRD29epBXyGHX2BS+bvwWQaKXgUoh4Pvvv8e3334LZ2dn7NixAwBgY2ODb775BqNHj2YIIKJqGRnqoXeHpx/jif2dINHLSKWJgQ8fPkTbtm0BQOnSQFtbW+Tm5mqmMiIiItIolUKAnZ0dTp48WWV5bGws3nqLl/YQERG9ilT6OGDMmDEIDw+Hl5cXysvLMWfOHFy8eBHnz5/H8uXLNV0jERERaYBKZwJ69eqFmJgYWFlZoWPHjsjJyYGLiwv27duHnj17arpGIiIi0gCVzgTExsbigw8+gJOTk6brISIiIi1R6UzAF198gcePH2u6FiIiItIilc4EjB8/HtOmTUNgYCCaNGkCfX3lzVq14h2/iIiIXjUqhYC5c+cCAA4dOqRYJpFIIAgCJBIJMjIyNFMdERERaYxKIeDnn3/WdB1ERESkZSqFgGbNmlW7XC6XY8iQIdi8ebNaiyIiIiJsUEh/AAAajklEQVTNUykEFBcXY926dUhPT4dMJlMsz83NRUFBgcaKIyIiIs1R6eqAOXPm4PDhw7Czs8Mff/yBd999F3K5HPXr18eGDRs0XSMRERFpgEpnAk6cOIF9+/bB0tISO3bswLRp0wAAq1evxi+//AKpVKrRIomIiEj9VDoTUF5eDktLSwCAvr4+SktLAQDDhg3Dpk2bNFcdERERaYxKIcDe3h7Lly9HWVkZ3nnnHWzduhUAcOPGDUUgICIioleLSiEgKioKBw8eRHl5OcLCwrB06VK4uLggJCQEAwcO1HSNREREpAEqzQlwdHRU3CioR48eiIuLQ2ZmJpo3b462bdtqtEAiIiLSDJVCwJMnT5QeN23aFE2bNlWM1a9fX/2VERERkUapFAJcXV0hkUhqHOdtg4mIiF49KoWAf18BUFFRgVu3biEuLg6ffvqpRgojIiIizVIpBHh4eFRZ1rFjR3Tq1AmTJ0+Gj4+P2gsjIiIizVLp6oCaWFtb49KlS+qqhYiIiLRIpTMB1X1BUElJCX755RfY2tqqvSgiIiLSPJVCwHfffVdlmZGREWxtbTFjxgy1F0VERESap1IIOHr0qKbrICIiIi1TKQQcP35c5Sf09vZ+7mKIiIhIe1QKAePHj4dMJoMgCErLJRKJ0jKJRMJ7BhAREb0iVAoBy5YtQ2JiIsaMGQNbW1vI5XJcunQJGzZsQEBAAHx9fTVdJxEREamZSiFg8eLF2LVrF0xNTRXLnJycMGfOHHzwwQcMAURERK8gle4TkJ2djeLi4irLi4uLkZeXp/aiiIiISPNUOhPQqVMnjBgxAiEhIWjatCkkEgnu3buHHTt2oHPnzpqukYiIiDRApRCwZMkSfPvtt9ixYwfu3bsHQRDw5ptvolu3bvjss880XSMRERFpgEohoGHDhoiIiEBERISm6yEiIiIteWYIyMjIgJmZGZo1awYAuHHjBtatW4eioiL06NED/fr103iRREREpH61Tgw8fvw4goODkZaWBgB48uQJhg4dinPnzqFBgwaYN28e9u3bp5VCiYiISL1qPRPw9ddfY+rUqfDz8wMAJCYmori4GPv374epqSm6d++Ob7/9FgEBAVoploiIiNSn1jMBmZmZCA4OVjw+ceIEvLy8FPcL8PLywuXLlzVbIREREWnEM+8TYGRkpPg5NTUV7dq1UzzW19evcithIiIiejXUGgKaNGmCS5cuAQDS09ORnZ2NTp06KcavX78OCwsLzVZIREREGlHrnAB/f39MmTIFAQEB2LNnD9zd3WFnZwcAKCoqwtKlS9G1a1dt1ElERERqVmsICAsLQ35+PmJjY9G6dWvMmDFDMbZs2TJcu3YN8+bN03iRREREpH4S4Tk/1M/OzoalpSUMDAzUXZPG5OQU6nT/1tamOq9Bl9g/+2f/7F+sdNm/tbVpjWMq3TGwOjY2Ns+7KREREb0EVPoWQSIiInr9MAQQERGJlFZDwNmzZzF48GC4ubmhc+fOiIyMRE5OTrXrxsTEwM/PD87OzggMDMTZs2cVY0VFRZg/fz68vb3h4uKCMWPG1Pg8REREVD2thYD8/HyMHDkSPXv2RFJSEuLj45GTk4NZs2ZVWTc2NhZffvkl5s6di5SUFAwZMgSjR49GQUEBAGDBggVITk5GTEwMfvvtN7z55puYPHmytlohIiJ6LWgtBMhkMsyYMQPDhg2DgYEBrKys0LNnT2RmZlZZ9+jRo+jVqxc8PDxgaGiIAQMGoFWrVkhMTFSMDx8+HG+//TYaNmyI6dOnIzU1FTdv3tRWO0RERK88rYUAa2trBAUFAQAEQcDVq1exZ88e9OnTp9r1JRKJ0mMzMzNkZGRUO25kZARjY2OlcSIiIqrdc18i+LwyMzMRFBQEuVyO4OBgTJw4sco6Pj4+mDt3LgYMGABnZ2ecPHkSqamp6NKli2J8w4YNaNeuHaytrbF+/XqUlZUhLy+v1n1bWJhAX19PI32pqrbrNcWA/bN/MWP/7P9l89w3C3oRgiDg2rVrmD17NiwtLbFy5coq42vXrsVPP/2EwsJC9OzZE0ZGRnj8+DEWL16M/Px8LFiwAL/++isMDAwwZMgQJCYmIjQ0FAMHDqxxv7q+UQVvlsH+2T/7Fyv2/5rdLOhFSCQStGzZEpGRkQgNDUVOTg6sra2VxseOHYuxY8cqlo0fP17xvQXm5uZYtGiR0nNu2LABjRs31kr9RERErwOtzQk4ePAgBgwYoLzzek93r6+vnEWuX7+OI0eOKB7LZDKkpKTA3d0dAJCSkqJ0yeBff/2FwsJCtG3bVlPlExERvXa0FgLc3Nxw8+ZNrF69GiUlJXjw4AGio6Ph5uYGCwsL+Pn5ISkpCQBw//59REZG4s8//4RMJsPixYvRqFEjxZyA06dPIyoqCvfv38fDhw8xb948DBw4EGZmZtpqh4iI6JWntRBgY2ODDRs24MSJE/Dw8EDfvn1hZmaGFStWAHj67r+4uBgA4OnpiYkTJyI8PBweHh64fv06vv76a+jpPZ3UN3r0aLi4uMDf3x9+fn5o3bo1pk2bpq1WiIiIXgs6mRioK7qelMKJMeyf/bN/sWL/L+fEQH53ABERkUgxBBAREYkUQwAREZFIMQQQERGJFEMAERGRSDEEEBERiRRDABERkUgxBBAREYkUQwAREZFIMQQQERGJFEMAERGRSDEEEBERiRRDABERkUgxBBAREYkUQwAREZFIMQQQERGJFEMAERGRSDEEEBERiRRDABERkUgxBBAREYkUQwAREZFIMQQQERGJFEMAERGRSDEEEBERiRRDABERkUgxBBAREYkUQwAREZFIMQQQERGJFEMAERGRSDEEEBERiRRDABERkUgxBBAREYkUQwAREZFIMQQQERGJFEMAERGRSDEEEBERiRRDABERkUgxBBAREYkUQwAREZFIMQQQERGJFEMAERGRSDEEEBERiRRDABERkUgxBBAREYkUQwAREZFIMQQQERGJlFZDwNmzZzF48GC4ubmhc+fOiIyMRE5OTrXrxsTEwM/PD87OzggMDMTZs2cVYyUlJZg3bx68vb3h6uqKwMBAHDlyRFttEBERvRa0FgLy8/MxcuRI9OzZE0lJSYiPj0dOTg5mzZpVZd3Y2Fh8+eWXmDt3LlJSUjBkyBCMHj0aBQUFAICVK1ciOTkZW7duxZkzZ/Dpp59iwoQJuHbtmrbaISIieuVpLQTIZDLMmDEDw4YNg4GBAaysrNCzZ09kZmZWWffo0aPo1asXPDw8YGhoiAEDBqBVq1ZITEwEAKSlpcHLywtNmzaFnp4e/P39YWhoiMuXL2urHSIiolee1kKAtbU1goKCAACCIODq1avYs2cP+vTpU+36EolE6bGZmRkyMjIAAL6+vjh69Chu3LiBiooK7N+/H/r6+mjfvr1mmyAiInqN6Gt7h5mZmQgKCoJcLkdwcDAmTpxYZR0fHx/MnTsXAwYMgLOzM06ePInU1FR06dIFADBy5EhcvnwZvXr1gkQigYmJCZYvXw5LS8ta921hYQJ9fT2N9KUqa2tTne5f19g/+xcz9s/+XzYSQRAEbe9UEARcu3YNs2fPhqWlJVauXFllfO3atfjpp59QWFiInj17wsjICI8fP8bixYuxZs0aJCQkYMWKFWjSpAkOHDiA+fPnIz4+Hs2bN69xvzk5hZpurVbW1qY6r0GX2D/7Z//sX6x02X9t4UMnlwhKJBK0bNkSkZGRSEhIqHKFgEQiwdixY3H06FGkpKRgwYIFePDgARo3bgzg6ZUDI0eORIsWLVC/fn0EBQXB1tYWCQkJumiHiIjolaS1EHDw4EEMGDBAeef1nu5eX1/5U4nr168rXfInk8mQkpICd3d3AIBcLkdFRYXSNv9+TERERLXTWghwc3PDzZs3sXr1apSUlODBgweIjo6Gm5sbLCws4Ofnh6SkJADA/fv3ERkZiT///BMymQyLFy9Go0aNFHMCfH198cMPP+D27dsoKytDXFwcrl+/Dh8fH221Q0RE9MrTWgiwsbHBhg0bcOLECXh4eKBv374wMzPDihUrADx9919cXAwA8PT0xMSJExEeHg4PDw9cv34dX3/9NfT0nk7qmzFjBtq1a4fBgwfDw8MDP/zwA6Kjo9GqVStttUNERPTK08nEQF3R9aQUToxh/+yf/YsV++fEQCIiInqJMAQQERGJFEMAERGRSDEEEBERiRRDABERkUgxBBAREYkUQwAREZFIMQQQERGJFEMAERGRSDEEEBERiRRDABERkUgxBBAREYkUQwAREZFIMQQQERGJFEMAERGRSDEEEBERiRRDABERkUgxBBAREYkUQwAREZFIMQQQERGJFEMAERGRSDEEEBERiRRDABERkUgxBBAREYkUQwAREZFIMQQQERGJFEMAERGRSDEEEBERiZREEARB10UQERGR9vFMABERkUgxBBAREYkUQwAREZFIMQQQERGJFEMAERGRSDEEEBERiRRDABERkUgxBNTBxYsXERAQAF9f31rXi4mJgZ+fH5ydnREYGIizZ88qxkpKSjBv3jx4e3vD1dUVgYGBOHLkiGLc19cXjo6OeO+99xT/ffrppxrrqS7U0X9RURHmz58Pb29vuLi4YMyYMcjJyVGMFxQUYNKkSejSpQs6deqESZMmobCwUGM91YU2+n+Zj/+dO3cQHh6ODh06oEOHDpgwYQKys7OrXTc5ORkhISFwc3ODn58ftm7dqjS+efNm9O7dG25ubggJCcGZM2cUYzKZDHPmzEG3bt3g6emJMWPG4N69exrtTRXa6n/IkCFwcHBQ+h3o06ePRntThTr7r6iowKpVq+Dg4IDdu3crjYnh+NfWv9aPv0Aq2b9/v9ClSxdh7Nixgo+PT43r7dmzR3BxcRGSkpKE0tJSYdeuXYKHh4eQn58vCIIgfPHFF0JAQIBw584doby8XNi/f7/g4OAgXL16VRAEQfDx8RF27dqllZ7qQl39T5s2Tejbt69w8+ZNobCwUJg5c6YwdOhQxfbh4eHCyJEjhZycHCE3N1cYOXKkMHHiRI339yza6v9lPf6CIAgBAQHCpEmThMLCQiE3N1cYOnSoMGrUqCrr3b9/X3B1dRU2b94sPHnyREhNTRXc3NyE48ePC4IgCMeOHRPc3NyElJQUoaSkRNi6davg5uYm5OTkCILw9P+R/v37C7du3RIKCgqEqKgoITg4WKu9Vkdb/Q8ePFhYtWqVVntThbr6f/LkifDhhx8K4eHhgpubW5Xf99f9+D+rf20ff54JUFFxcTG2b9+Ojh071rre0aNH0atXL3h4eMDQ0BADBgxAq1atkJiYCABIS0uDl5cXmjZtCj09Pfj7+8PQ0BCXL1/WRhvPTV39Hz16FMOHD8fbb7+Nhg0bYvr06UhNTcXNmzeRm5uLw4cPIzIyEo0aNYKVlRUmTpyIxMREPHz4UBtt1kgb/b/MCgoK4OTkhClTpqBhw4awsrJCSEgIUlJSqqwbHx+PZs2a4T//+Q+MjY3h5uaG/v37Y9u2bQCArVu3IjAwEO3atYORkRFCQ0PRpEkT7Nu3D+Xl5di5cyfGjh2L5s2bw9TUFFOmTMH58+eRkZGh7bYVtNX/y0qd/RcXF6NPnz5YtWoV9PX1lbYVw/GvrX9dYAhQ0cCBA9G0aVOV1pVIJEqPzczMFL/Avr6+OHr0KG7cuIGKigrs378f+vr6aN++vWL9/fv3w8/PD66urhgzZkyNp5y0SV39/3vcyMgIxsbGyMjIQEZGBiQSCdq0aaMYb9OmDQRB0Ok/AIB2+q/0Mh5/MzMzLFy4EDY2Nopld+/eVXpc6cKFC3B0dFRa5uDggLS0NMW4g4NDteO3bt1CYWGh0rilpSUaN26s2F4XtNV/pdOnT6Nfv35wdXXFoEGDcPXqVXW2U2fq7N/S0hJDhgypdj9iOP619V9Jm8efIUDNfHx8kJCQgJSUFMhkMhw7dgypqanIy8sDAIwcORIuLi7o1asXHB0dMXPmTCxduhSWlpYAgHfffRcODg7YuXMnEhMTUVpaivDwcF22VCfP6t/HxwcbNmzA7du3UVJSgtWrV6OsrAx5eXnIy8tDgwYNoKenp3g+AwMDNGjQAI8ePdJVS3XyIv0Dr87xv3btGtauXYuxY8dWGcvLy4OZmZnSsjfeeENxDKsbNzc3V/wOVD7+9/jL9Dugqf4BoGXLlmjZsiU2btyIX375Bc2aNcOnn36K0tJSDXVTdy/Sf23EcPyfRdvHX/fnIl4zH3zwAe7evYupU6eisLAQPXv2RJ8+ffD48WMAwJo1a3DhwgUcPHgQTZo0wYEDBzBx4kTEx8ejefPmWL16teK5TE1N8b///Q/+/v64du0aWrRooau2VPas/qOiorBgwQKEhITAwMAAQ4YMQevWrRWnxYRX/PusXrT/V+H4p6WlYfTo0RgxYgT69u1b7Tovehxf5t8DTfc/e/Zspceff/45PD09kZycjK5duz7386oLj//rdfwZAtRMIpFg7NixSglx/PjxsLOzA/B05vjUqVMV/6AHBQUhJiYGCQkJ1c4Cb9asGQDg/v37L80fgdo8q39zc3MsWrRIaZsNGzagcePGkEgkePz4McrKymBgYAAAKCsrw+PHj2FlZaW1Hl7Ei/RfnZft+J84cQITJ07EpEmT8J///KfadSwsLBTv6Co9evRIcQwtLCyqvCvKy8uDpaWl4ozYv99NVY7rmqb7r07Dhg1hbm6O+/fvq6GDF6OO/msjhuNfV5o+/vw4QM2uX7+udMmfTCZDSkoK3N3dAQByuRwVFRVK21Q+vnPnDmbNmgWZTKYYq/wsqHnz5pouXS2e1X9KSorSJXN//fUXCgsL0bZtW7z77ruQSCT466+/FOPp6enQ09Or8hnqy+pF+n/Zj/+5c+cQERGBRYsW1fgPIAC89957SE9PV1qWlpYGZ2dnAICTk1OV8fPnz8PFxQXNmzeHubm50nh2djbu3bsHFxcXNXZTd9rov6ioCLNnz1aaB/Lw4UM8fPhQ578D6uq/NmI4/rXRxfFnCFADPz8/JCUlAXj6ji0yMhJ//vknZDIZFi9ejEaNGqFLly4Ank4M/OGHH3D79m2UlZUhLi4O169fh4+PD6ysrHD06FF88cUXKC4uRnZ2NhYuXIhu3bop3hG+jOrS/+nTpxEVFYX79+/j4cOHmDdvHgYOHAgzMzNYWlqid+/eWLFiBR48eICcnBx8+eWX6NevX5XPCF8m6ur/ZT7+5eXlmD59OsLDw9GjR48q48OGDUN8fDwAoF+/fsjJycHmzZtRWlqKpKQk7N27VzEZatCgQYiPj8eZM2dQWlqK77//Hvn5+QgICICenh5CQ0Oxdu1aZGVloaCgAIsXL0aHDh3QunVrrfb8T9rqv2HDhjh37hzmz5+PvLw85OfnY86cOZBKpWjXrp1We/4ndfZfGzEc/9ro5Phr7WLEV9z7778vODk5CQ4ODoJUKhWcnJwEJycnISsrS5BKpcLRo0cV63733XdC586dBWdnZ2HkyJFCVlaWYqywsFCYM2eO4OXlJbi4uAiBgYFK22ZmZgrDhw8X3N3dBTc3NyEqKkpxjbkuqav/0tJSYerUqYK7u7vQvn17YebMmUJJSYlivLCwUJg8ebLg5uYmuLu7C1FRUUJxcbFWe62Otvp/WY9/SkqKUt///C8rK0vw8fERYmJiFOufOXNGCAwMFJycnITu3bsLe/bsUXq+7du3Cz4+PoKTk5MwcOBA4dy5c4oxmUwmzJs3T/Dw8BBcXFyEcePGCQ8ePNBar9XRZv937twRxo0bp+g/LCxMuHfvntZ6rY46+9+zZ49iW6lUKjg4OAhOTk7CjBkzBEF4/Y//s/rX9vGXCMJLPAODiIiINIYfBxAREYkUQwAREZFIMQQQERGJFEMAERGRSDEEEBERiRRDABERkUgxBBCRxn300UdYunSprssgon9hCCCiWg0dOhRRUVHVjv36669wdHR8Ke5rT0R1xxBARLUKCQlBYmKi4psQ/2n37t3w9vbGm2++qYPKiOhFMQQQUa3ef/99GBoa4uDBg0rL8/Pz8fPPPyMkJAQlJSWYOXMmunTpAldXVwQHB+PcuXPVPt/kyZMRERGheFxeXg57e3v8+uuvAICSkhLMnTsX3bp1g4uLCwYNGoTMzEzNNUgkYgwBRFQrQ0ND9O/fH7t371Zavm/fPlhaWqJr165Yt24d/vjjD+zduxfJyclwd3fHxIkTn2t/ixcvxl9//YXt27cjKSkJ7u7uGDt2bJVv3ySiF8cQQETPFBISgj/++AM3btxQLNuzZw8GDBgAPT09hIWFYfv27bCwsICBgQH8/f3x999/4+HDh3XaT3l5Ofbs2YOxY8fCxsYGRkZGmDBhAvLy8pCcnKzmrohIX9cFENHLr1WrVnBxccGePXsQERGBy5cv48KFC1i5ciUAIDc3FwsWLEBycrLS3AGZTFan/eTm5qK4uBhjx46FRCJRLJfL5bh79656miEiBYYAIlJJcHAwVq1ahQkTJmD37t3o1KkTmjVrBgCYOHEi6tevj9jYWDRp0gTp6ekICgpS6XnlcrniZyMjIwDAtm3b4OTkpP4miEgJPw4gIpX07t0bRUVFSElJwf79+xESEqIYS0tLw4cffogmTZoAAC5cuFDj8xgZGeHJkyeKx7du3VL8bGFhATMzM1y8eFFpm6ysLHW1QUT/wBBARCoxMTFBQEAAli5divLycvj6+irG3nrrLZw7dw5lZWU4deoUDh8+DADIzs6u8jy2trY4d+4c7t69i6KiIqxfvx4GBgaK8Y8++ghr1qzBlStXUF5ejs2bNyMwMBBFRUWab5JIZBgCiEhlISEhOH/+PD744AOlP9yzZs3C4cOH4eHhgR9++AGLFi1Cp06dMHz4cFy+fFnpOT788EM4ODigd+/eCAwMhL+/P0xMTBTj48aNg5eXFwYNGoT27dtj7969WLduHRo2bKi1PonEQiIIgqDrIoiIiEj7eCaAiIhIpBgCiIiIRIohgIiISKQYAoiIiESKIYCIiEikGAKIiIhEiiGAiIhIpBgCiIiIRIohgIiISKT+P98sDXRspOJIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import paddle\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "\r\n",
    "plt.style.use('seaborn')\r\n",
    "fig,ax = plt.subplots()\r\n",
    "ax.scatter(2,4,s=200)\r\n",
    "\r\n",
    "# 设置图表标题标签\r\n",
    "ax.set_title(\"Square number\",fontsize=24)\r\n",
    "ax.set_xlabel(\"Value\",fontsize=14)\r\n",
    "ax.set_ylabel(\"Square of value\",fontsize=14)\r\n",
    "\r\n",
    "# 设置刻度标记的大小\r\n",
    "ax.tick_params(axis='both',which='major',labelsize=14)\r\n",
    "\r\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 使用scatter()绘制一系列点\n",
    "要绘制一系列的点，可向scatter()传递两个分别包含x值和y值的列表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fd89e3fbd90>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import paddle\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "\r\n",
    "x_values = [1,2,3,4,5]\r\n",
    "y_values = [1,4,9,16,25]\r\n",
    "\r\n",
    "plt.style.use('seaborn')\r\n",
    "fig,ax = plt.subplots()\r\n",
    "ax.scatter(x_values,y_values,s = 100)\r\n",
    "# 设置图表标题并给坐标轴指定标签\r\n",
    "--snip--"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 自动计算数据\n",
    "绘制1000个点的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1100, 0, 1100000]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import paddle\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "\r\n",
    "x_values = range(1,1001)\r\n",
    "y_values = [x**2 for x in x_values]\r\n",
    "\r\n",
    "plt.style.use('seaborn')\r\n",
    "fig,ax = plt.subplots()\r\n",
    "ax.scatter(x_values,y_values,s = 10)\r\n",
    "\r\n",
    "# 设置每个坐标轴的取值范围\r\n",
    "ax.axis([0,1100,0,1100000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 自定义颜色\n",
    "修改数据点颜色，向scatter()传递参数c,并将其设置为要使用的颜色名称"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ax.scatter(x_values,y_values,c='red',s=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "还可以使用 RGB 颜色模式自定义颜色，传递参数c，并设置为一个元组，三个0~1的小数值，分别表示红绿蓝颜色的分量，例如下面由淡绿色点组成的散点图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ax.scatter(x_values,y_values,c=(0,0.8,0),s=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "值越接近0，指定的颜色越深；值越接近1，指定的颜色越浅"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 使用颜色映射\n",
    "从起始颜色渐变到结束颜色，在可视化中，颜色映射用于突出数据的规律"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "模块pyplot内置了一组颜色映射，下面演示如何根据每个点的y值来设置其颜色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import MutableMapping\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import Iterable, Mapping\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  from collections import Sized\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2349: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  if isinstance(obj, collections.Iterator):\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2366: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n",
      "  return list(data) if isinstance(data, collections.MappingView) else data\n"
     ]
    }
   ],
   "source": [
    "import paddle\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "fig,ax = plt.subplots()\r\n",
    "\r\n",
    "x_values = range(1,1001)\r\n",
    "y_values = [x**2 for x in x_values]\r\n",
    "\r\n",
    "ax.scatter(x_values,y_values,c=y_values,cmap=plt.cm.Blues,s=10)\r\n",
    "\r\n",
    "#然后设置标题和标签"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "将参数c设置成了一个y值列表，并使用参数cmap告诉pyplot使用哪个颜色映射，将y值较小的点显示为浅蓝色，y值较大的点显示为深蓝色"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 自动保存图表\n",
    "要将图表保存到文件中，可调用plt.show()替换为plt.savefig()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.savefig('squares_plot.png',bbox_inches='tight')"
   ]
  }
 ],
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